This invention relates generally to the field of seismic data processing, and specifically to determining reflection geometry in seismic gather data. This information can be used to determine stacking velocity parameters.
In seismic data processing, different pieces of data (time samples from seismic traces) presumed to originate from the same location on a subsurface reflector are combined, or stacked, to enhance the amplitude of desired reflected information relative to noise. This process of stacking requires a parameter, called the stacking velocity, which describes the change in traveltime of the reflected energy as a function of the distance between the seismic source and receiver. This stacking velocity somehow must be selected (or xe2x80x9cpickedxe2x80x9d) throughout the subterranean spatial region represented in the seismic survey. Picking stacking velocities for seismic data is often the most time consuming portion of seismic data processing.
Existing methods for picking stacking velocities consist of first producing velocity semblance, velocity sweep, or velocity events displays at each velocity analysis location in the survey. A brief description of each of these types of display follows:
Velocity Semblance Display
To produce a velocity semblance display, one first measures the trace-to-trace coherence (or coherency), within a common midpoint (CMP) gather of seismic traces, as a function of both moveout velocity and time. This coherency data may be plotted, on a computer monitor for example, as an image with time (or depth) as the vertical axis, velocity as the horizontal axis, and with the pixel color spectrum used as a measure of the coherency value. Peaks in this coherency, as a function of time and velocity, are taken to be potential points on the velocity versus time (or depth) function at the x-y position of that common midpoint. The coherency peaks are assumed to represent subterranean reflectors or horizons, and the time (or depth) value for each peak represents the zero-offset travel time (or depth) of the horizon at that x-y position.
The coherency can be computed using a number of methods. Common methods include simply moveout correcting the traces and computing the trace-to-trace semblance.
Velocity Sweep Display
A velocity sweep is a stack, of several adjacent CMP gathers, for a suite of different stacking velocities. The stacks, of several adjacent CMP gathers, at one velocity in the sweep are called a velocity sweep panel. The velocity sweep displays are again interpreted, in a manner similar to that described above, to produce a velocity versus time function. However, the velocity interpreter can usually pick better velocities from a velocity sweep display than from a semblance display, because:
The interpretation is based not only on discerning trace-to-trace coherency within a CMP gather, but also on the spatial (i.e., lateral) trace-to-trace coherency between the CMP locations. This spatial trace-to-trace coherency, rather than the coherency within a CMP gather, is much more important to the geologic interpreter when performing geologic interpretation of seismic data. Thus, optimizing spatial trace-to-trace coherency should be the primary goal of stacking velocity analysis.
The human eye can often observe optimal spatial trace-to-trace coherency in areas where poor signal-to-noise ratios make discerning optimal trace-to-trace coherency within a single CMP gather impossible.
Velocity sweep displays provide some geologic information that the velocity interpreter can use to separate signal from noise. For example, multiple reflections (xe2x80x9cmultiplesxe2x80x9d) often have different dip than the primary geologic reflections one wishes to optimize.
Velocity Events Display
In order to improve the efficiency of the velocity interpretation process, velocity semblance or velocity sweep data are often pre-analyzed using an automatic picking program. See, for example J. H. Bodine, J. N. Gallagher, and J. H. Wright, xe2x80x9cGeophysical Exploration Using Velocity Spectra Regional Coherency Peaksxe2x80x9d, U.S. Pat. No. 4,984,220 (1991). This automatic picking program searches the velocity semblance for peaks, and outputs a file containing only the time, velocity and coherency value of the peaks. These (time, velocity, coherency) data are called velocity xe2x80x9ceventsxe2x80x9d because each of them may represent reflected energy from a specific subsurface reflection or series of reflections. These data are plotted as a scatter plot with time as the vertical axis, velocity as the horizontal axis, and some aspect of the scatter symbol (e.g., color or size) used to indicate coherency. The automatically picked events themselves do not produce a satisfactory velocity function, because they are usually quite noisy. The events must be interpreted to produce a velocity function for the velocity analysis location.
Interpretation of Velocity Displays
After producing the velocity semblance, sweeps or events, these data are interpreted at each location in the survey individually. The only extra information, regarding adjacent velocity analysis locations, that is usually displayed are velocity functions that may previously have been picked at those adjacent locations. This conventional method for picking stacking velocities has several problems:
The simple fact that velocities must be analyzed at each location individually makes this a time consuming process.
Since the velocity interpreter cannot view the velocity analysis displays for a large region, it is difficult to pick velocities that do not have some unreasonable lateral velocity variations. Therefore, the interpreted velocity model must often be edited several times to remove these unreasonable lateral variations.
Velocity semblance and events displays show only coherency within a CMP gather, not spatial coherency. On the other hand, spatial coherency is the attribute that has primary importance to the geologic interpreter, and is more robust in the presence of low signal-to-noise ratios.
Velocity sweep displays have poor velocity precision, because they display panels of several traces for each velocity. This implies that only a few velocities (on the order of 25) can be displayed before the display becomes unwieldy.
Since conventional velocity analysis techniques use 2-D displays, with time being one dimension of the display, it is difficult to pick more than one xe2x80x9cattributexe2x80x9d (i.e., any measurement based on seismic data, such as velocity, reflector dip or a non-hyperbolic moveout parameter) of the seismic data. The attribute that is usually picked is hyperbolic moveout velocity. Conventional methods have difficulty picking attributes beyond this hyperbolic moveout velocity, such as non-hyperbolic moveout parameters that may result from anisotropy or lateral velocity variation.
Bodine et al., in their patent referenced above, attempt to overcome the problem of unreasonably large lateral velocity variations. Their patent treats the velocity data, from one seismic line, as a cube with vertical axis being time, one lateral axis being velocity, and the other lateral axis being location along the seismic line. By extracting various slices from this cube, and interpreting those slices, they can produce smoother velocity models than those produced by the conventional method discussed above. However, their method has several shortcomings:
Since their method only works with slices of the data, the velocity interpreter is never presented with a single display that shows the velocity information corresponding to a large region. Thus, it is still difficult to produce velocities that vary smoothly in all dimensions.
For 3-D data, each xe2x80x9cinlinexe2x80x9d in the 3-D volume would have to be analyzed individually by their method. Thus, it would be difficult to ensure that the velocity model is smooth in the direction perpendicular to the inline direction (i.e., the xe2x80x9ccrosslinexe2x80x9d direction).
The only attribute used by their method to pick velocity is coherency within CMP gathers. As discussed above, other attributes can be more diagnostic. In particular, spatial coherency is of primary interest to the geologic interpreter.
D. Doicin, C. Johnson and N. Hargreaves have presented an automated method for interpreting velocity events (EAEG/EAPG 57th Conference and Technical Exhibition, Glasglow, Scotland, Extended Abstracts Volume 1, 1995). In their method, velocity events are computed as in Bodine""s above-referenced patent, except that they are computed at every time and spatial (x,y) position sampled in the seismic data. These events are then grouped together in a horizon-consistent manner, and any events that are not contained in a group having a lateral extent greater than a user-specified threshold are eliminated. Events falling outside of a user-specified window are also eliminated. Doicin, et al.""s method overcomes some of the problems discussed above. Since it is automatic, it reduces interpretation time analyzing the events, and precision isn""t compromised due to display limitations. Their method also imposes some constraint on the spatial consistency of events, so that some of the unreasonably large lateral variations are eliminated. However, their method has the following limitations:
While they do use spatial coherency to eliminate events, they don""t incorporate spatial coherency into their velocity events themselves. In low signal-to-noise ratio areas this will lead to missed events that could be visible to the human eye on a velocity sweep display.
Their events do not incorporate any spatial attributes (e.g. reflector dip)which can be very useful for discerning signal from noise.
They don""t provide methods for quickly editing the results of their automated picking.
From the foregoing, it can be seen that an improved method for picking stacking velocities is needed. Such a method should be capable of presenting multiple attributes, including spatial coherency, for simultaneous interpretation, thereby generating better stacking velocity picks as well as enabling velocity picking to be performed in areas of poor signal-to-noise ratio. Such a method should make use of geologic xe2x80x9cbiasxe2x80x9d, such as expected variability in velocity, local reflector dip, and other seismic attributes over large subsurface regions, to discriminate the velocity of primary reflections from that of noise. Such a method should generate a globally consistent stacking velocity model in minimal time and at minimal expense, thus enabling more economic velocity interpretation of 3-D surveys. The present invention satisfies these needs.
The present invention is a method for determining stacking velocity parameters or other reflection geometry information from seismic data, which in one embodiment comprises the steps of: (a) computing the coherence, with respect to selected seismic attributes (e.g., moveout velocity, inline dip and crossline dip), of a set of adjacent common midpoint (CMP) gathers at selected subterranean spatial locations; (b) picking seismic events at the peaks of this coherence function; and (c) editing the picked events with the objective of eliminating all events except those representing primary seismic reflections. If a smooth velocity model is fitted to the edited events, the resulting velocities can be used for stacking the seismic data. Alternatively, the edited events can be used for other purposes such as input data for a tomographic velocity updating procedure. Preferably, step (b) is performed by automated means and step (c) is performed by a combination of automated means and human intervention.
In some embodiments, the coherence is calculated not only within each CMP gather, but also laterally between neighboring CMP gathers, thus mimicking by automated means the ability of the human eye to discern velocity events from noise in poor signal-to-noise areas and resulting in high quality event picking.
Velocity events produced by this method can have additional attributes (e.g., dip and coherence) which allow for quality control of the events not only based on coherence within a gather but also on the geologic consistency of events over larger regions.
In some embodiments of the present invention, the editing process is a combination of automated editing where events are deleted based on pre-set parameters and thresholds, and human intervention. Setting up the human editing, the automated process groups the events into sets characterized by smooth variations in their attributes over large regions. This grouping can be done by statistical means or by artificial intelligence means. The interpreter is then able to identify entire groups of events as corresponding to unwanted events such as multiple reflections, and accordingly delete them. The editing by the interpreter is greatly enhanced in the preferred embodiments of the present invention by the use of three-dimensional visualization techniques, which allow the interpreter to interpret velocities globally rather than one event at a time, thereby easily avoiding introducing unwanted rapid lateral velocity variations. The three-dimensional visualization display also presents more than one attribute at a time for the interpreter to consider.